{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 熊猫(pandas)简介\n",
    "* 来源: [官方英文新手教程](https://pandas.pydata.org/pandas-docs/version/1.0.2/getting_started/index.html#getting-started)\n",
    "* 课堂教学方式：\n",
    "   * 分段式以英文新手教程的内容做示范及说明\n",
    "   * 课堂上以实际中文数据做操练，每段约10-15分钟\n",
    "   * 抽学生联mic自播说明难点及成果点，教师总结\n",
    "* 关於新旧内容\n",
    "   * 旧Python内容若有不熟处，会按课堂教学实际状况记录后，在往后的课程补充\n",
    "   * 新Pandas内容请大家以下操演的心法及剑法学习\n",
    "       * **将代码当成人类语言**用**片语化**记忆，并配合\n",
    "       * **将数据处理输入输出**用**视觉语言**记忆\n",
    "       \n",
    "![02_io_readwrite](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "\n",
    "* 以上为官方英文新手教程例子，数据处理\n",
    "  * 输入有  **read_叉叉**  方法  \n",
    "  * 输出有  **to_叉叉**  方法  \n",
    "  * 可配合多种数据做输入输出\n",
    "  * 片语法记忆：以常用之csv及xlsx输入输出为例\n",
    "     * 数据框.read_csv( 档名, 参数 ... )\n",
    "     * 数据框.read_excel( 档名, 参数 ... )\n",
    "     * 数据框.to_csv( 档名, 参数 ... )\n",
    "     * 数据框.to_excel( 档名, 参数 ... )\n",
    "  * 人类语言片语化，可组合\n",
    "     * 输入 数据框.read_支援的格式名\n",
    "     * 输出 数据框.to_支援的格式名\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%html\n",
    "# <style>\n",
    "# /* 本电子讲义使用之CSS */\n",
    "# div.code_cell {\n",
    "#     background-color: #e5f1fe;\n",
    "# }\n",
    "# div.cell.selected {\n",
    "#     background-color: #effee2;\n",
    "#     font-size: 2rem;\n",
    "#     line-height: 2.4rem;\n",
    "# }\n",
    "# div.cell.selected .rendered_html table {\n",
    "#     font-size: 2rem !important;\n",
    "#     line-height: 2.4rem !important;\n",
    "# }\n",
    "# .rendered_html pre code {\n",
    "#     background-color: #C4E4ff;   \n",
    "#     padding: 2px 25px;\n",
    "# }\n",
    "# .rendered_html pre {\n",
    "#     background-color: #99c9ff;\n",
    "# }\n",
    "# div.code_cell .CodeMirror {\n",
    "#     font-size: 2rem !important;\n",
    "#     line-height: 2.4rem !important;\n",
    "# }\n",
    "# .rendered_html img, .rendered_html svg {\n",
    "#     max-width: 45%;\n",
    "#     height: auto;\n",
    "#     float: right;\n",
    "# }\n",
    "# /* Gradient transparent - color - transparent */\n",
    "# hr {\n",
    "#     border: 0;\n",
    "#     border-bottom: 1px dashed #ccc;\n",
    "# }\n",
    "# .emoticon{\n",
    "#     font-size: 5rem;\n",
    "#     line-height: 4.4rem;\n",
    "#     text-align: center;\n",
    "#     vertical-align: middle;\n",
    "# }\n",
    "# </style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![01_table_dataframe.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/01_table_dataframe.svg)\n",
    "\n",
    "# 本周内容\n",
    "本周内容共分6段(5段+1段下周预告)，新手请认真记忆并操练，老手有彩蛋项目去实践\n",
    "\n",
    "* [熊猫处理什么样的数据？](#熊猫处理什么样的数据？)\n",
    "\n",
    "> <mark>框框框</mark>，探索，清理和处理数据在Pandas中，数据表称为DataFrame，数据科学家**变数variables**通常放(竖着的列)column，**观察observations**通常放(横着的行)row\n",
    "\n",
    "<br/><br/><br/>\n",
    "\n",
    "-----\n",
    "\n",
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "\n",
    "* [如何读写表格数据？](#如何读写表格数据？)\n",
    "\n",
    "> <mark>读读读，写写写</mark>，数据科学家不想浪费时间编程去处理不同数据格式，所以pandas集成了常用的现成的文件格式或数据源（csv，excel，sql，json，parquet等）...\n",
    "\n",
    "-----\n",
    "\n",
    "![03_subset_columns_rows.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/03_subset_columns_rows.svg)\n",
    "\n",
    "* [如何选择表格的子集？](#如何选择表格的子集？)\n",
    "\n",
    "> <mark>切切切</mark>，**切片** (英文叫slice) 是数据科学家找突破点的重要工具，是她们的数据解剖刀...\n",
    "\n",
    "-----\n",
    "\n",
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "\n",
    "* [如何在熊猫中绘图？](#如何在熊猫中绘图？)\n",
    "\n",
    "> <mark>绘绘绘</mark>，**绘图** ( 数据框.plot() ) 是数据科学家**以数据框为中心**的代码实践，减少以图表类型为开头的编程思维来作图\n",
    "\n",
    "-----\n",
    "\n",
    "![05_newcolumn_2.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/05_newcolumn_2.svg)\n",
    "\n",
    "* [如何从现有列创建派生新列？](#如何从现有列创建派生新列？)\n",
    "\n",
    "> <mark>列列列</mark>，派生新列意谓着变数variables的进一部转换，是数据科学家按步就班做ETL的过程，新派生列就是**变数variables**的转换\n",
    "\n",
    "-----\n",
    "\n",
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "\n",
    "* [如何计算汇总描述性统计信息？](#如何计算汇总描述性统计信息？)\n",
    "\n",
    "> <mark>算算算</mark>，描述性统计竟然代码可以这麽容易....，但难的仍是在数据科学家的数据定义及解释上\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![01_table_dataframe.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/01_table_dataframe.svg)\n",
    "## 熊猫处理什么样的数据？\n",
    "\n",
    "> <mark>框框框</mark>，探索，清理和处理数据在Pandas中，数据表称为DataFrame\n",
    "\n",
    "\n",
    "对数据科学家来说: \n",
    "\n",
    "* 竖着的列column通常放**变数variables**\n",
    "* 横着的行row通常放**观察observations**\n",
    "\n",
    "对数据及信息管理人员来说:\n",
    "* 表格数据（例如存储在电子表格或数据库中的数据）是很常见的，最主流的数据结构和查询语言是SQL\n",
    "* 树状文本数据（例如HTML, XML, JSON数据）是很常见的，HTML/XML最主流的查询语言是xpath\n",
    "\n",
    "<div class=\"emoticon\">🐷🙈🙉🙊🐷</div>\n",
    "\n",
    "<div class=\"emoticon\">🙈🐷🙉🐷🙊</div>\n",
    "\n",
    "<div class=\"emoticon\">🐷🙈🐷🙊🐷</div>\n",
    "\n",
    "----- \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 框框框的代码片语\n",
    "框框框的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "框框 = pd.DataFrame ( {\n",
    "        \"变数X\": [\"观察X1\", \"观察X2\", \"观察X3\", \"观察X4\"],\n",
    "        \"变数Y\": [\"观察Y1\", \"观察Y2\", \"观察Y3\", \"观察Y4\"],\n",
    "        \"变数Z\": [\"观察Z1\", \"观察Z2\", \"观察Z3\", \"观察Z4\"],\n",
    "      } )```\n",
    "\n",
    "记得，像人类语言一样，说的清楚，人就可以读的比较清楚....\n",
    "\n",
    "* pd.DataFrame 的主流参数是字典\n",
    "* 该字典的键keys是由变数构成，相当於表格中一行行的标题\n",
    "* 该字典的值values是由观察的列表构成，相当於表格中一行行的数据\n",
    "* 表格真的要是表格, 该字典的每个观察的列表数量必需齐一\n",
    "\n",
    "[小贴士] 最后一个逗点可有可无(为什麽?)，在pandas情境下最好留(为什麽?)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 框框框的课堂练习\n",
    "新手请认真记忆并操练 \n",
    "#### 练习A1-框框框的课堂建构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变数X</th>\n",
       "      <th>变数Y</th>\n",
       "      <th>变数Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>观察X1</td>\n",
       "      <td>观察Y1</td>\n",
       "      <td>观察Z1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>观察X2</td>\n",
       "      <td>观察Y2</td>\n",
       "      <td>观察Z2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>观察X3</td>\n",
       "      <td>观察Y3</td>\n",
       "      <td>观察Z3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>观察X4</td>\n",
       "      <td>观察Y4</td>\n",
       "      <td>观察Z4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    变数X   变数Y   变数Z\n",
       "0  观察X1  观察Y1  观察Z1\n",
       "1  观察X2  观察Y2  观察Z2\n",
       "2  观察X3  观察Y3  观察Z3\n",
       "3  观察X4  观察Y4  观察Z4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "做别的事去了\n"
     ]
    }
   ],
   "source": [
    "# A1\n",
    "框框 = pd.DataFrame ( {\n",
    "        \"变数X\": [\"观察X1\", \"观察X2\", \"观察X3\", \"观察X4\"],\n",
    "        \"变数Y\": [\"观察Y1\", \"观察Y2\", \"观察Y3\", \"观察Y4\"],\n",
    "        \"变数Z\": [\"观察Z1\", \"观察Z2\", \"观察Z3\", \"观察Z4\"],\n",
    "      } )\n",
    "display (框框)\n",
    "print(\"做别的事去了\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变数X</th>\n",
       "      <th>变数Y</th>\n",
       "      <th>变数Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>观察X1</td>\n",
       "      <td>观察Y1</td>\n",
       "      <td>观察Z1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>观察X2</td>\n",
       "      <td>观察Y2</td>\n",
       "      <td>观察Z2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>观察X3</td>\n",
       "      <td>观察Y3</td>\n",
       "      <td>观察Z3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>观察X4</td>\n",
       "      <td>观察Y4</td>\n",
       "      <td>观察Z4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    变数X   变数Y   变数Z\n",
       "0  观察X1  观察Y1  观察Z1\n",
       "1  观察X2  观察Y2  观察Z2\n",
       "2  观察X3  观察Y3  观察Z3\n",
       "3  观察X4  观察Y4  观察Z4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A1 dict\n",
    "# 先弄字典, 再弄 框框 的写法片语\n",
    "字典 = {\n",
    "        \"变数X\": [\"观察X1\", \"观察X2\", \"观察X3\", \"观察X4\"],    \n",
    "        \"变数Y\": [\"观察Y1\", \"观察Y2\", \"观察Y3\", \"观察Y4\"],\n",
    "        \"变数Z\": [\"观察Z1\", \"观察Z2\", \"观察Z3\", \"观察Z4\"],\n",
    "       }\n",
    "\n",
    "框框 = pd.DataFrame ( 字典 )\n",
    "框框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    变数X   变数Y   变数Z\n",
      "0  观察X1  观察Y1  观察Z1\n",
      "1  观察X2  观察Y2  观察Z2\n",
      "2  观察X3  观察Y3  观察Z3\n",
      "3  观察X4  观察Y4  观察Z4\n"
     ]
    }
   ],
   "source": [
    "# A1 print\n",
    "# 在ipynb用 print比较不上看，因为是文字text输出为主\n",
    "print (框框)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变数X</th>\n",
       "      <th>变数Y</th>\n",
       "      <th>变数Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>观察X1</td>\n",
       "      <td>观察Y1</td>\n",
       "      <td>观察Z1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>观察X2</td>\n",
       "      <td>观察Y2</td>\n",
       "      <td>观察Z2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>观察X3</td>\n",
       "      <td>观察Y3</td>\n",
       "      <td>观察Z3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>观察X4</td>\n",
       "      <td>观察Y4</td>\n",
       "      <td>观察Z4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    变数X   变数Y   变数Z\n",
       "0  观察X1  观察Y1  观察Z1\n",
       "1  观察X2  观察Y2  观察Z2\n",
       "2  观察X3  观察Y3  观察Z3\n",
       "3  观察X4  观察Y4  观察Z4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A1 display\n",
    "# 在ipynb可用 display 比较上看\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "# 從 IPython.display 模塊 導入使用 display和HTML\n",
    "display (框框)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 练习A1_post-框框框的课堂后练习\n",
    "\n",
    "老手有彩蛋项目可实践"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变数X</th>\n",
       "      <th>变数Y</th>\n",
       "      <th>变数Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>观察X1</td>\n",
       "      <td>观察Y1</td>\n",
       "      <td>观察Z1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>观察X2</td>\n",
       "      <td>观察Y2</td>\n",
       "      <td>观察Z2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>观察X3</td>\n",
       "      <td>观察Y3</td>\n",
       "      <td>观察Z3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>观察X4</td>\n",
       "      <td>观察Y4</td>\n",
       "      <td>观察Z4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    变数X   变数Y   变数Z\n",
       "0  观察X1  观察Y1  观察Z1\n",
       "1  观察X2  观察Y2  观察Z2\n",
       "2  观察X3  观察Y3  观察Z3\n",
       "3  观察X4  观察Y4  观察Z4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A1 bonus\n",
    "# 已有此字典, 你能用代码生产同样的框框吗？\n",
    "字典 = {\n",
    "        \"变数\": [\"观察1\", \"观察2\", \"观察3\"],\n",
    "       }\n",
    "\n",
    "# 你的代码\n",
    "\n",
    "框框"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "新手请认真记忆并操练，\n",
    "#### 练习A2-**框框框**(DataFrame)的取变数成 **系列** (Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    观察X1\n",
       "1    观察X2\n",
       "2    观察X3\n",
       "3    观察X4\n",
       "Name: 变数X, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series\n",
    "# first slice\n",
    "框框 [\"变数X\"]  # [] 像字典取值, 改值从列表升级为有**索引的序列**\n",
    "# object 对象/物件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['观察X1', '观察X2', '观察X3', '观察X4'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series values\n",
    "框框 [\"变数X\"].values # 值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 Series index\n",
    "框框 [\"变数X\"].index # 索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(框框 [\"变数X\"].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "变数X    观察X4\n",
       "变数Y    观察Y4\n",
       "变数Z    观察Z4\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A2 DataFrame .loc[]\n",
    "# 用 .loc[] 取列, 相当於所有变数的某一次观察\n",
    "框框.loc[3]   # loc = location  row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['观察X1', '观察X2', '观察X3', '观察X4']\n",
      "['观察X1', '观察X2', '观察X3', '观察X4']\n",
      "['观察X1', '观察X2', '观察X3', '观察X4']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# A2 Series to_list()\n",
    "# 降阶打击, 变列表的方法\n",
    "print ( 框框 [\"变数X\"].to_list() )    \n",
    "print ( list(框框 [\"变数X\"]) )\n",
    "print ( list(框框 [\"变数X\"].values) )\n",
    "print ( list(框框 [\"变数X\"].index) )\n",
    "\n",
    "# 区分读/写代码: \n",
    "# 你可以挑一个你比较常用的\"说法\", \n",
    "# 但你看到别人不同的说法时, 你需要知道是同一件事"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>变数X</th>\n",
       "      <th>变数Y</th>\n",
       "      <th>变数Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>观察X1</td>\n",
       "      <td>观察Y1</td>\n",
       "      <td>观察Z1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>观察X2</td>\n",
       "      <td>观察Y2</td>\n",
       "      <td>观察Z2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>观察X3</td>\n",
       "      <td>观察Y3</td>\n",
       "      <td>观察Z3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>观察X4</td>\n",
       "      <td>观察Y4</td>\n",
       "      <td>观察Z4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    变数X   变数Y   变数Z\n",
       "0  观察X1  观察Y1  观察Z1\n",
       "1  观察X2  观察Y2  观察Z2\n",
       "2  观察X3  观察Y3  观察Z3\n",
       "3  观察X4  观察Y4  观察Z4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "框框 = pd.DataFrame ( {\n",
    "        \"变数X\": [\"观察X1\", \"观察X2\", \"观察X3\", \"观察X4\"],\n",
    "        \"变数Y\": [\"观察Y1\", \"观察Y2\", \"观察Y3\", \"观察Y4\"],\n",
    "        \"变数Z\": [\"观察Z1\", \"观察Z2\", \"观察Z3\", \"观察Z4\"],\n",
    "      } )\n",
    "display (框框)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "变数X    观察X1\n",
       "变数Y    观察Y1\n",
       "变数Z    观察Z1\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "框框.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![01_table_dataframe.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/01_table_dataframe.svg)\n",
    "### 框框框的小结\n",
    "以下为廖老师示范，其它小结需要自己做小结\n",
    "1. 对数据科学来说，表格数据的习惯是变数variables与观察observations\n",
    "2. 在Pandas框框框的建构来说，就是可以把变数和观察用索引的方式使用\n",
    "3. 框框框的字典取的是某个变数所有观察, 用.loc[]取數次观察的所有变数\n",
    "  * .loc[] 取  观察, 行 column\n",
    "  * [\"\"] 取  变数, 列 row\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "## 如何读写表格数据？\n",
    "读读读，写写写，数据科学家不想浪费时间编程去处理不同数据格式，所以pandas集成了常用的现成的文件格式或数据源（csv，excel，sql，json，parquet等）..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 框框框的课堂练习\n",
    "新手请认真记忆并操练 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读读读的代码片语\n",
    "读读读的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "读到csv = pd.read_csv(\"路径档案名\", encoding=\"utf8\")\n",
    "读到tsv = pd.read_csv(\"路径档案名\", encoding=\"utf8\", sep=\"\\t\")\n",
    "读到excel = pd.read_excel(\"路径档案名\", sheet_name=\"分页名称\")\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "你可否查到[最新的文档](https://pandas.pydata.org/pandas-docs/version/1.0.2/)的说明，用markdown语法编辑在此，并看一些有什麽参数可以使用，自己学者做笔记\n",
    "\n",
    "* pd.read_csv\n",
    "* pd.read_excel\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "      <td>美国</td>\n",
       "      <td>纽约</td>\n",
       "      <td>云计算</td>\n",
       "      <td>Jim Schaper</td>\n",
       "      <td>2002</td>\n",
       "      <td>Golden Gate Capital, Koch Equity Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "      <td>美国</td>\n",
       "      <td>旧金山</td>\n",
       "      <td>消费品</td>\n",
       "      <td>Adam Bowen, James Monsees, Kevin Burns, Tim Da...</td>\n",
       "      <td>2015</td>\n",
       "      <td>M13, Timothy Davis, Evolution VC Partners, Tig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称   Company Name  估值（亿人民币）  国家   城市     行业  \\\n",
       "0   1       蚂蚁金服  Ant Financial     10000  中国   杭州   金融科技   \n",
       "1   2       字节跳动      Bytedance      5000  中国   北京  媒体和娱乐   \n",
       "2   3       滴滴出行   Didi Chuxing      3600  中国   北京   共享经济   \n",
       "3   4      Infor          Infor      3500  美国   纽约    云计算   \n",
       "4   5  JUUL Labs      JUUL Labs      3400  美国  旧金山    消费品   \n",
       "\n",
       "                                             掌门人/创始人  成立年份  \\\n",
       "0                                                井贤栋  2014   \n",
       "1                                                张一鸣  2012   \n",
       "2                                                 程维  2012   \n",
       "3                                        Jim Schaper  2002   \n",
       "4  Adam Bowen, James Monsees, Kevin Burns, Tim Da...  2015   \n",
       "\n",
       "                                              部分投资机构  \n",
       "0                                     春华资本、中投海外、红杉资本  \n",
       "1                                红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2                             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  \n",
       "3       Golden Gate Capital, Koch Equity Development  \n",
       "4  M13, Timothy Davis, Evolution VC Partners, Tig...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B1 20春_pandas_week02_hurun_unicorn.tsv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn.tsv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "0   1  蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1   2  字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2   3  滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "3   6   陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "4  11  微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "\n",
       "                   部分投资机构   region  \n",
       "0          春华资本、中投海外、红杉资本  环杭州湾大湾区  \n",
       "1     红杉资本、海纳亚洲、纪源资本、启明创投    渤海大湾区  \n",
       "2  腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本    渤海大湾区  \n",
       "3     摩根士丹利、中银集团、国泰君安（香港）  环杭州湾大湾区  \n",
       "4             腾讯、华平投资、淡马锡   粤港澳大湾区  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B2 20春_pandas_week02_hurun_unicorn_more.csv\n",
    "df = pd.read_csv(\"20春_pandas_week02_hurun_unicorn_more.csv\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>494</td>\n",
       "      <td>494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "      <td>25</td>\n",
       "      <td>485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Raise</td>\n",
       "      <td>DJI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "      <td>红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206</td>\n",
       "      <td>81</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>180.977733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>238.805668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.234818</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>91.073191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.158537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.792477</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>224.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>264.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                排名   企业名称 Company Name      估值（亿人民币）   国家   城市    行业 掌门人/创始人  \\\n",
       "count   494.000000    494          494    494.000000  494  494   494     494   \n",
       "unique         NaN    494          494           NaN   24  120    25     485   \n",
       "top            NaN  Raise          DJI           NaN   中国   北京  电子商务       -   \n",
       "freq           NaN      1            1           NaN  206   81    68       3   \n",
       "mean    180.977733    NaN          NaN    238.805668  NaN  NaN   NaN     NaN   \n",
       "std      91.073191    NaN          NaN    623.158537  NaN  NaN   NaN     NaN   \n",
       "min       1.000000    NaN          NaN     70.000000  NaN  NaN   NaN     NaN   \n",
       "25%      84.000000    NaN          NaN     70.000000  NaN  NaN   NaN     NaN   \n",
       "50%     224.000000    NaN          NaN    100.000000  NaN  NaN   NaN     NaN   \n",
       "75%     264.000000    NaN          NaN    200.000000  NaN  NaN   NaN     NaN   \n",
       "max     264.000000    NaN          NaN  10000.000000  NaN  NaN   NaN     NaN   \n",
       "\n",
       "               成立年份 部分投资机构  \n",
       "count    494.000000    494  \n",
       "unique          NaN    489  \n",
       "top             NaN   红杉资本  \n",
       "freq            NaN      3  \n",
       "mean    2011.234818    NaN  \n",
       "std        3.792477    NaN  \n",
       "min     2000.000000    NaN  \n",
       "25%     2009.000000    NaN  \n",
       "50%     2012.000000    NaN  \n",
       "75%     2014.000000    NaN  \n",
       "max     2019.000000    NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B3 20春_pandas_week02_hurun_unicorn.xlsx\n",
    "df = pd.read_excel(\"20春_pandas_week02_hurun_unicorn.xlsx\", encoding=\"utf8\", sheet_name=\"独角兽\")\n",
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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Brown,\\\\u00a0Steven McKnight\",\"69\":\"\\\\u5218\\\\u81ea\\\\u9e3f\",\"70\":\"John Bicket, Sanjit Biswas\",\"71\":\"Thierry Cruanes, Marcin Zukowski, Benoit Dageville\",\"72\":\"Daniel Macklin,\\\\u00a0Ian Brady,\\\\u00a0James Finnigan,\\\\u00a0Michael Cagney\",\"73\":\"Kristo Kaarmann, Taavet Hinrikus\",\"74\":\"Albert Albert,\\\\u00a0Derianto Kusuma,\\\\u00a0Ferry Unardi\",\"75\":\"Ariel Cohen,\\\\u00a0Ilan Twig\",\"76\":\"\\\\u5468\\\\u5251\",\"77\":\"\\\\u859b\\\\u654f\",\"78\":\"\\\\u6c88\\\\u6656\",\"79\":\"\\\\u4f55\\\\u5c0f\\\\u9e4f\",\"80\":\"\\\\u5de6\\\\u6656\",\"81\":\"Deepinder Goyal,\\\\u00a0Pankaj Chaddah\",\"82\":\"Jason Austin, Lex Greensill\",\"83\":\"Jeffrey Kaditz,\\\\u00a0Max Levchin,\\\\u00a0Nathan Gettings\",\"84\":\"Ankur Kothari,\\\\u00a0Mihir Shukla,\\\\u00a0Neeti Mehta\",\"85\":\"\\\\u4e8e\\\\u51ac\",\"86\":\"Henrique Dubugras,\\\\u00a0Pedro Franceschi\",\"87\":\"\\\\u5f20\\\\u6960\\\\u8d53\",\"88\":\"Cameron Adams,\\\\u00a0Cliff Obrecht,\\\\u00a0Melanie Perkins\",\"89\":\"\\\\u7530\\\\u6797\",\"90\":\"Jeremy Allaire,\\\\u00a0Sean Neville\",\"91\":\"\\\\u5468\\\\u66e6\",\"92\":\"Jay Kreps,\\\\u00a0Jun Rao,\\\\u00a0Neha Narkhede\",\"93\":\"\\\\u5218\\\\u8363\",\"94\":\"Ali Ghodsi,\\\\u00a0Andy Konwinski,\\\\u00a0Ion Stoica,\\\\u00a0Matei Zaharia,\\\\u00a0Patrick Wendell,\\\\u00a0Reynold Xin,\\\\u00a0Scott Shenker\",\"95\":\"\\\\u9648\\\\u5c11\\\\u6770\",\"96\":\"\\\\u6731\\\\u5149\",\"97\":\"Ryan Petersen\",\"98\":\"Doug Hirsch,\\\\u00a0Scott Marlette,\\\\u00a0Trevor Bezdek\",\"99\":\"\\\\u6768\\\\u78ca\",\"100\":\"\\\\u5218\\\\u4e16\\\\u9ad8\",\"101\":\"\\\\u4f59\\\\u5efa\\\\u519b\",\"102\":\"\\\\u4f59\\\\u51ef\",\"103\":\"\\\\u5f90\\\\u79c0\\\\u8d24\",\"104\":\"Fritz H. Wolff,\\\\u00a0Jim Davidson,\\\\u00a0Michael Marks\",\"105\":\"\\\\u80e1\\\\u6c38\",\"106\":\"\\\\u5f90\\\\u6b63\",\"107\":\"Gary Dolman,\\\\u00a0Jason Bates,\\\\u00a0Jonas Huckestein,\\\\u00a0Paul Rippon,\\\\u00a0Tom Blomfield\",\"108\":\"Maximilian Tayenthal,\\\\u00a0Valentin Stalf\",\"109\":\"Dave Ferguson, Jiajun Zhu\",\"110\":\"Joel Perlman,\\\\u00a0Rishi Khosla\",\"111\":\"Greg Wyler\",\"112\":\"Joshua Kushner,\\\\u00a0Mario Schlosser\",\"113\":\"Vijay Shekhar Sharma\",\"114\":\"William Hockey,\\\\u00a0Zachary Perret\",\"115\":\"Craig Courtemanche\",\"116\":\"\\\\u9f50\\\\u5411\\\\u4e1c\",\"117\":\"Alexis Ohanian,\\\\u00a0Steve Huffman\",\"118\":\"David Baszucki\",\"119\":\"Arvind Jain, Arvind Nithrakashyap, Bipul Sinha, Soham Mazumdar\",\"120\":\"\\\\u6c88\\\\u6d77\\\\u5bc5\",\"121\":\"Alex Fenkell,\\\\u00a0Jordan Katzman\",\"122\":\"\\\\u59da\\\\u519b\\\\u7ea2\",\"123\":\"Nandan Reddy,\\\\u00a0Rahul Jaimini,\\\\u00a0Sriharsha Majety\",\"124\":\"Eric Lefkofsky\",\"125\":\"Aman Narang,\\\\u00a0Jonathan Grimm,\\\\u00a0Steve Fredette\",\"126\":\"David Helgason,\\\\u00a0Joachim Ante,\\\\u00a0Nicholas Francis\",\"127\":\"\\\\u6bdb\\\\u5927\\\\u5e86\",\"128\":\"\\\\u7c73\\\\u96ef\\\\u5a1f\",\"129\":\"Bong Jin Kim\",\"130\":\"\\\\u6bdb\\\\u6587\\\\u8d85\",\"131\":\"\\\\u91d1\\\\u5149\\\\u78ca\",\"132\":\"\\\\u97e9\\\\u5764\",\"133\":\"\\\\u536b\\\\u4fca\",\"134\":\"\\\\u674e\\\\u52c7\",\"135\":\"Jesse Levinson,\\\\u00a0Tim Kentley-Klay\",\"136\":\"\\\\u4faf\\\\u5efa\\\\u5f6c\",\"137\":\"Anne Wojcicki,\\\\u00a0Linda Avey,\\\\u00a0Paul Cusenza\",\"138\":\"Zia Chishti\",\"139\":\"\\\\u9648\\\\u96ea\\\\u5cf0\",\"140\":\"Adam Foroughi,\\\\u00a0Andrew Karam,\\\\u00a0John Krystynak\",\"141\":\"\\\\u674e\\\\u6d9b\",\"142\":\"Dustin Moskovitz,\\\\u00a0Justin Rosenstein\",\"143\":\"Chris Urmson,\\\\u00a0J. Andrew Bagnell,\\\\u00a0Sterling Anderson\",\"144\":\"Al Goldstein,\\\\u00a0John Sun,\\\\u00a0Paul Zhang\",\"145\":\"Brent Gutekunst,\\\\u00a0Ivan Griffin,\\\\u00a0Ken Mulvany,\\\\u00a0Michael Brennan\",\"146\":\"Karthik Ganapathy, Ajay Kaushal, MN Srinivasu\",\"147\":\"\\\\u8d75\\\\u957f\\\\u9e4f\\\\u3001\\\\u4f55\\\\u4e00\",\"148\":\"Travis VanderZanden\",\"149\":\"Francis Nappez,\\\\u00a0Fr\\\\u00e9d\\\\u00e9ric Mazzella,\\\\u00a0Nicolas Brusson\",\"150\":\"Brendan Blumer\",\"151\":\"John Johnson,\\\\u00a0Jonah Peretti\",\"152\":\"\\\\u6bd5\\\\u798f\\\\u5eb7\",\"153\":\"\\\\u9648\\\\u5929\\\\u77f3\",\"154\":\"\\\\u7530\\\\u660e\",\"155\":\"Joseph M. DeSimone,\\\\u00a0Philip DeSimone\",\"156\":\"Henry Ward,\\\\u00a0Manu Kumar\",\"157\":\"Guillaume Pousaz\",\"158\":\"\\\\u674e\\\\u60f3\",\"159\":\"Chris Britt,\\\\u00a0Ryan King\",\"160\":\"Ingmar Hoerr\",\"161\":\"Dave Palmer,\\\\u00a0Emily Orton,\\\\u00a0Jack Stockdale,\\\\u00a0Nicole Eagan,\\\\u00a0Poppy Gustafsson\",\"162\":\"Jeff Kinsey,\\\\u00a0Theodore Bailey\",\"163\":\"Bhavesh Manglani,\\\\u00a0Kapil Bharati,\\\\u00a0Mohit Tandon,\\\\u00a0Sahil Barua,\\\\u00a0Suraj Saharan\",\"164\":\"Greg Orlowski,\\\\u00a0William Shu\",\"165\":\"Chris Schuh,\\\\u00a0Ely Sachs,\\\\u00a0Emanuel M. Sachs,\\\\u00a0John Hart,\\\\u00a0Jonah Myerberg,\\\\u00a0Ric Fulop,\\\\u00a0Rick Chin,\\\\u00a0Yet-Ming Chiang\",\"166\":\"Ed Park,\\\\u00a0Jeremy Delinsky,\\\\u00a0Todd Park\",\"167\":\"Dominic Williams\",\"168\":\"Jason Citron\",\"169\":\"Andr\\\\u00e9 Schw\\\\u00e4mmlein,\\\\u00a0Daniel Krauss,\\\\u00a0Jochen Engert\",\"170\":\"Girish Mathrubootham,\\\\u00a0Shan Krishnasamy\",\"171\":\"Dave Waiser,\\\\u00a0Roi More\",\"172\":\"Nigel Toon,\\\\u00a0Simon Knowles\",\"173\":\"Edward Kim,\\\\u00a0Joshua Reeves,\\\\u00a0Tomer London\",\"174\":\"Armon Dadgar,\\\\u00a0Mitchell Hashimoto\",\"175\":\"Charles A. Taylor,\\\\u00a0Christopher K. Zarins\",\"176\":\"Monte Casino,\\\\u00a0Patrick Brown\",\"177\":\"Herman Narula,\\\\u00a0Peter Lipka,\\\\u00a0Rob Whitehead\",\"178\":\"Moshe Yanai\",\"179\":\"David Elkington,\\\\u00a0Ken Krogue,\\\\u00a0Rob Christensen\",\"180\":\"Ben Nadel,\\\\u00a0Clark Valberg\",\"181\":\"\\\\u6768\\\\u79cb\\\\u747e\",\"182\":\"Gerald Blackie\",\"183\":\"\\\\u738b\\\\u80b2\\\\u6797\",\"184\":\"Benny Landa\",\"185\":\"Daniel Schreiber,\\\\u00a0Shai Wininger\",\"186\":\"Adam Zhang,\\\\u00a0Brad Bao,\\\\u00a0Charlie Gao,\\\\u00a0Toby Sun\",\"187\":\"\\\\u9648\\\\u7f61\",\"188\":\"Jason Gardner\",\"189\":\"\\\\u53f6\\\\u56fd\\\\u5bcc\",\"190\":\"Eran Zinman,\\\\u00a0Roy Mann\",\"191\":\"Michael A Liberty\",\"192\":\"Dhiraj C Rajaram\",\"193\":\"Patrick Soon-Shiong\",\"194\":\"Adam Ginsburg,\\\\u00a0David Wiesen,\\\\u00a0Madison Bell,\\\\u00a0Nirav Tolia,\\\\u00a0Prakash Janakiraman,\\\\u00a0Sarah Leary\",\"195\":\"Craig Weiss\",\"196\":\"Paolo Cerruti,\\\\u00a0Peter Carlsson\",\"197\":\"Gordon Sanghera,\\\\u00a0Hagan Bayley\",\"198\":\"Adam Bowen,\\\\u00a0James Monsees\",\"199\":\"\\\\u9648\\\\u5b87\",\"200\":\"\\\\u5f6d\\\\u519b \\\\u3001\\\\u697c\\\\u5929\\\\u57ce\",\"201\":\"Bastian Lehmann,\\\\u00a0Sam Street,\\\\u00a0Sean Plaice\",\"202\":\"Daisuke Okanohara,\\\\u00a0Toru Nishikawa\",\"203\":\"Louay Eldada,\\\\u00a0Yu Tianyue\",\"204\":\"Adam D\\\\u2019Angelo,\\\\u00a0Charlie Cheever\",\"205\":\"\\\\u6c6a\\\\u83b9\",\"206\":\"Sumant Sinha\",\"207\":\"Nikolay Storonsky,\\\\u00a0Vlad Yatsenko\",\"208\":\"Calvin French-Owen,\\\\u00a0Ian Storm Taylor,\\\\u00a0Ilya Volodarsky,\\\\u00a0Peter Reinhardt\",\"209\":\"Ara Mahdessian,\\\\u00a0Vahe Kuzoyan\",\"210\":\"Jeff Arnold,\\\\u00a0Mehmet Oz\",\"211\":\"Ragy Thomas\",\"212\":\"Anthony Casalena\",\"213\":\"Robert Simonds,\\\\u00a0William McGlashan\",\"214\":\"\\\\u5f20\\\\u8fd1\\\\u4e1c\",\"215\":\"\\\\u4fde\\\\u6c38\\\\u798f\",\"216\":\"Bradley Keywell\",\"217\":\"Andrew Hunt,\\\\u00a0David Gilboa,\\\\u00a0Jeffrey 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Collins\",\"223\":\"\\\\u8c37\\\\u5cf0\",\"224\":\"\\\\u5218\\\\u91d1\\\\u826f\",\"225\":\"\\\\u6c88\\\\u535a\\\\u9633\",\"226\":\"\\\\u80e1\\\\u4e1c\",\"227\":\"\\\\u5f20\\\\u6d77\\\\u4eae\",\"228\":\"\\\\u8c2d\\\\u9f99\",\"229\":\"\\\\u674e\\\\u950b\",\"230\":\"\\\\u5e94\\\\u4e66\\\\u5cad\",\"231\":\"\\\\u5f20\\\\u4e00\\\\u6625\",\"232\":\"\\\\u674e\\\\u6aac\",\"233\":\"\\\\u5218\\\\u5929\\\\u6587\",\"234\":\"\\\\u8f9b\\\\u5229\\\\u519b\",\"235\":\"\\\\u4f55\\\\u529b\",\"236\":\"\\\\u6c6a\\\\u5efa\\\\u56fd\",\"237\":\"\\\\u738b\\\\u5fd7\\\\u8c6a\",\"238\":\"\\\\u6d2a\\\\u6e05\\\\u534e\",\"239\":\"\\\\u5218\\\\u6960\",\"240\":\"\\\\u845b\\\\u5c9a\",\"241\":\"\\\\u59ec\\\\u6653\\\\u6668\",\"242\":\"\\\\u6731\\\\u4e00\\\\u95fb\",\"243\":\"\\\\u9ad8\\\\u7984\\\\u5cf0\",\"244\":\"\\\\u7530\\\\u5b81\",\"245\":\"\\\\u674e\\\\u5efa\\\\u5168\",\"246\":\"\\\\u89e3\\\\u5c45\\\\u5fd7\",\"247\":\"\\\\u8bb8\\\\u4ef0\\\\u5929\",\"248\":\"\\\\u5f20\\\\u52c7\",\"249\":\"\\\\u9ec4\\\\u5b8f\\\\u751f\",\"250\":\"\\\\u5c45\\\\u9759\",\"251\":\"\\\\u848b\\\\u97ec\",\"252\":\"\\\\u738b\\\\u56fd\\\\u5f6c\",\"253\":\"\\\\u9648\\\\u654f\",\"254\":\"\\\\u7f57\\\\u519b\",\"255\":\"\\\\u738b\\\\u5b66\\\\u96c6\",\"256\":\"\\\\u738b\\\\u73c2\",\"257\":\"\\\\u9ece\\\\u745e\\\\u521a\",\"258\":\"\\\\u674e\\\\u9769\",\"259\":\"\\\\u9648\\\\u9a70\",\"260\":\"\\\\u5f20\\\\u7ee7\\\\u5b66\",\"261\":\"\\\\u6731\\\\u660e\\\\u8dc3\",\"262\":\"\\\\u738b\\\\u4e1c\",\"263\":\"\\\\u5f20\\\\u97f6\\\\u5cf0\",\"264\":\"Ben Hindson,\\\\u00a0Serge Saxonov\",\"265\":\"\\\\u5218\\\\u7545\",\"266\":\"\\\\u6768\\\\u9675\\\\u6c5f\",\"267\":\"\\\\u6234\\\\u6587\\\\u6e0a\",\"268\":\"\\\\u5b59\\\\u96f7\",\"269\":\"Sebastian Betz,\\\\u00a0Tarek Muller\",\"270\":\"Ash Ashutosh,\\\\u00a0David Chang\",\"271\":\"Doug Dohring\",\"272\":\"Andrew Ofstad,\\\\u00a0Emmett Nicholas,\\\\u00a0Howie Liu\",\"273\":\"Jack Zhang\",\"274\":\"\\\\u674e\\\\u6587\\\\u535a\",\"275\":\"\\\\u5f20\\\\u52c7\",\"276\":\"Joseph Zwillinger,\\\\u00a0Tim Brown\",\"277\":\"Robert Edward Grant\",\"278\":\"\\\\u738b\\\\u62e5\\\\u519b\",\"279\":\"\\\\u5409\\\\u670b\\\\u677e\",\"280\":\"Daniel Saks,\\\\u00a0Nicolas Desmarais\",\"281\":\"Eugenio Pace,\\\\u00a0Matias Woloski\",\"282\":\"Matt Mullenweg\",\"283\":\"Michael Praeger\",\"284\":\"Jen Rubio,\\\\u00a0Steph Korey\",\"285\":\"\\\\u90dd\\\\u98de\",\"286\":\"\\\\u5f20\\\\u826f\\\\u4f26\",\"287\":\"Abhinay Choudhari,\\\\u00a0Hari Menon,\\\\u00a0Vipul Parekh,\\\\u00a0VS 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Astra International Tbk - TSO Salemba\",\"23\":\"Alibaba Group, SoftBank, Berkshire Hathaway, Sapphire Ventures, Mountain Capital, Ant Financial\",\"24\":\"\\\\u817e\\\\u8baf\",\"25\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u4eca\\\\u65e5\\\\u8d44\\\\u672c\\\\u3001IDG\\\\u3001\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\",\"26\":\"SoftBank Investment Advisers, Altos Ventures, Sequoia Capital, BlackRock Private Equity Partners, Softbank, Maverick Ventures\",\"27\":\"IDG\\\\u3001\\\\u601d\\\\u4f70\\\\u76ca\\\\u3001\\\\u8f6f\\\\u94f6\\\\u6d77\\\\u5916\",\"28\":\"Formation 8, GGV Capital, Founders Fund, DST Global, Temasek Holdings\",\"29\":\"DFJ, Andreessen Horowitz, Tiger Global Management, IVP, Bank of Tokyo-Mitsubishi\",\"30\":\"Illumina, ARCH Venture Partners, 6 Dimensions Capital, Ally Bridge Group, Hillhouse Capital Group\\\\u00a0, HuangPu River Capital\",\"31\":\"Y Combinator, Sequoia Capital, Kleiner Perkins, Andreessen Horowitz, Tiger Global Management, Coatue Management, D1 capital partners, Whole Foods Market\",\"32\":\"Index Ventures, New Enterprise Associates, DST Global\",\"33\":\"Volkswagen, Ford\",\"34\":\"\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\",\"35\":\"IDG\\\\u3001\\\\u601d\\\\u4f70\\\\u76ca\",\"36\":\"Softbank Investment Advisors, QVT Financial, Viking Global Investors, Novaquest Capital Management, RTW Investments LLC\",\"37\":\"\\\\u5149\\\\u5927\\\\u63a7\\\\u80a1\\\\u3001\\\\u6df1\\\\u521b\\\\u6295\",\"38\":\"Andreessen Horowitz, Franklin Templeton Investments,\\\\u00a0Geodesic Capital,\\\\u00a0IVP (Institutional Venture Partners), TPG, TPG Growth, Wellington Management\",\"39\":\"Indonusa Dwitama, East Ventures, CyberAgent Capital, Beenos Partners, Softbank Ventures Asia, SoftBank Telecom Corp, Softbank Investment Advsors\",\"40\":\"Denso, Softbank Investment Advisors, Toyota Motor Corporation\",\"41\":\"Earlybird Venture Capital, Capital G, Sequoia Capital, Coatue Management, Accel\",\"42\":\"Aarin Capital, Sequoia Capital India, Chan Zuckerberg Initiative, Sofina, Verlinvest, Tencent Holdings, Naspers, General Atlantic, Qatar Investment Authority, Sovereign Wealth Funds\",\"43\":\"\\\\u817e\\\\u8baf\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u5149\\\\u901f\\\\u4e2d\\\\u56fd\\\\u3001\\\\u9ad8\\\\u74f4\\\\u8d44\\\\u672c\\\\u3001\\\\u4e91\\\\u5cf0\\\\u57fa\\\\u91d1\\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\",\"44\":\"Tamasek Holdings, Alibaba Group, Google, Saudi Arabia\\'s Public Investment Fund, NTT Docomo\",\"45\":\"Tiger Global Management, Hyundai Motor Company, Kia Motors, Sequoia Capital India, SoftBank Capital, DST Global, Baillie Gifford, Vanguard, Softbank, Falcon Edge Capital, Tekne Capital, Yes Bank, Sachin Bansal, Temasek Holdings, China Eurasian Economic Cooperation Fund, Eternal Yield International, Steadview Capital, Tencent Holdings, Sailing Capital\",\"46\":\"\\\\u9f0e\\\\u6656\\\\u6295\\\\u8d44\\\\u3001IDG\\\\u3001\\\\u4e2d\\\\u91d1\\\\u516c\\\\u53f8\",\"47\":\"\\\\u4e91\\\\u950b\\\\u57fa\\\\u91d1\\\\u3001\\\\u4e91\\\\u5cad\\\\u6295\\\\u8d44\\\\u3001\\\\u4e2d\\\\u91d1\\\\u516c\\\\u53f8\",\"48\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u9ad8\\\\u76db\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\\\\u3001\\\\u9ad8\\\\u74f4\\\\u8d44\\\\u672c\",\"49\":\"Tencent Holdings\",\"50\":\"Alibaba Group, Temasek Holding, Tesco, Rocket Internet\",\"51\":\"Y Combinator, Menlo Ventures, JP Morgan Partners\",\"52\":\"Greenoaks Capital, SoftBank, SoftBank Investment Advisers, Huazhu Hotels Group, Grab, Didi Chuxing, Airbnb\",\"53\":\"Core Innovation Capital, IDG Capital, Santander InnoVentures, SBI Investment\",\"54\":\"Ford Motor Company, Amazon\",\"55\":\"Artemis, Lewis Trust Group, Kohlberg Kravis Roberts, Balderton Capital, Merian Global Investors, William Currie Group\",\"56\":\"DN Capital, Piton Capital, DST Global, Princeville Global, SoftBank Investment Advisers\",\"57\":\"IVP (Institutional Venture Partners), Wellington Management, Fidelity, SoftBank Investment Advisers, Qatar Investment Authority\",\"58\":\"QED Investors, Susquehanna Growth Equity, CapitalG, SV Angel, Silver Lake Partners\",\"59\":\"Evergrande Health Industry Group, Birch Lake Partners\",\"60\":\"Zeev Ventures, Sequoia Capital, GGV Capital, New Enterprise Associates, ICONIQ Capital\",\"61\":\"Flagship Pioneering, Alaska Permanent Fund, Activant Capital, Investment Corporation of Dubai (ICD), Baillie Gifford\",\"62\":\"Investment AB \\\\u00d6resund, Sequoia Capital, General Atlantic, Creandum, Anders Holch Povlsen, Visa, Permira, H&M, Snoop Dogg\",\"63\":\"\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\\\\u3001\\\\u8054\\\\u60f3\\\\u4e4b\\\\u661f\\\\u3001\\\\u5efa\\\\u94f6\\\\u56fd\\\\u9645\\\\u3001\\\\u8682\\\\u8681\\\\u91d1\\\\u670d \\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\",\"64\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001DST\\\\u3001\\\\u4eac\\\\u4e1c\",\"65\":\"Alsop Louie Partners, Spark Capital, IVP (Institutional Venture Partners)\",\"66\":\"Sequoia Capital, Tiger Global Management, Founders Fund, Goldman Sachs, DST Global, Fortress Investment Group, Tencent Holdings\",\"67\":\"Khosla Ventures, GGV Capital, Access Technology Ventures, Norwest Venture Partners, Lennar Corporation, SoftBank Investment Advisers, General Atlantic\",\"68\":\"Tiger Global Management, L Catterton, Fidelity, Kleiner Perkins, True Ventures, Wellington Management\",\"69\":\"\\\\u4e2d\\\\u4fe1\\\\u4ea7\\\\u4e1a\\\\u57fa\\\\u91d1\\\\u3001\\\\u57fa\\\\u77f3\\\\u8d44\\\\u672c\\\\u3001IDG\",\"70\":\"Andreessen Horowitz, General Catalyst\",\"71\":\"Sutter Hill Ventures, Redpoint, Altimeter Capital, ICONIQ Capital, Sequoia Capital\",\"72\":\"Baseline Ventures, Morgan Stanley, The Bancorp, East West Bank, Discovery Capital, Third Point Ventures, Softbank, Silver Lake Partners, Qatar Investment Authority\",\"73\":\"Seedcamp, IA Ventures\\\\u00a0, Valar Ventures, Index Ventures, Andreessen Horowitz, Baillie Gifford, IVP (Institutional Venture Partners)\\\\u00a0, JP Morgan, Lead Edge Capital, Merian Global Investors, LHV Ventures, NatWest Bank, Lone Pine Capital, Vitruvian Partners\",\"74\":\"East Ventures, Global Founders Capital, GIC, Expedia\",\"75\":\"Zeev Ventures, Lightspeed Venture Partners, Andreessen Horowitz, 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Founders Capital, Morgan Stanley, GIC, Thrive Capital, HVF Labs\",\"84\":\"Goldman Sachs, SoftBank Investment Advisers, Workday Ventures\",\"85\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\\\\u3001\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\\\\u3001\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u4e07\\\\u8fbe\\\\u9662\\\\u7ebf\\\\u3001\\\\u817e\\\\u8baf\",\"86\":\"Y Combinator, Ribbit Capital, DST Global, Barclays Investment Bank, Kleiner Perkins , Greenoaks Capital\",\"87\":\"\\\\u9526\\\\u6c5f\\\\u96c6\\\\u56e2\\\\u3001\\\\u66be\\\\u6f9c\\\\u8d44\\\\u672c\",\"88\":\"Felicis Ventures, Blackbird Ventures (Australia), Sequoia Capital, Bond\\\\u00a0, General Catalyst\",\"89\":\"\\\\u5149\\\\u9645\\\\u8d44\\\\u672c\\\\u3001IDG\",\"90\":\"Breyer Capital, IDG Capital, Bitmain, Goldman Sachs Principal Strategic Investments\",\"91\":\"\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u5143\\\\u79be\\\\u539f\\\\u70b9\\\\u3001\\\\u524d\\\\u6d77\\\\u5174\\\\u65fa\",\"92\":\"Benchmark, Index Ventures, Sequoia Capital\",\"93\":\"\\\\u963f\\\\u91cc\\\\u5f71\\\\u4e1a\",\"94\":\"Andreessen Horowitz, New Enterprise Associates\",\"95\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u5357\\\\u5c71\\\\u8d44\\\\u672c\",\"96\":\"TPG\\\\u3001\\\\u51ef\\\\u96f7\\\\u6295\\\\u8d44\\\\uff0c\\\\u6cf0\\\\u5eb7\\\\u96c6\\\\u56e2\\\\u3001\\\\u519c\\\\u94f6\\\\u56fd\\\\u9645\",\"97\":\"Founders Fund, DST Global, SF Express, SoftBank Investment Advisers\",\"98\":\"Silver Lake Partners\",\"99\":\"\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\\\\u3001\\\\u78d0\\\\u8c37\\\\u521b\\\\u6295\\\\u3001\\\\u6109\\\\u60a6\\\\u8d44\\\\u672c\\\\u3001\\\\u8682\\\\u8681\\\\u91d1\\\\u670d\",\"100\":\"\\\\u534e\\\\u76d6\\\\u8d44\\\\u672c\",\"101\":\"\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\\\\u3001Sierra 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Partners.\",\"110\":\"EDBI, SoftBank Investment Advisers, NIBC Bank N.V., Indiabulls Housing Finance Limited\",\"111\":\"SoftBank, Qualcomm Ventures, Virgin Group\",\"112\":\"Thrive Capital, Founders Fund, Formation 8, CapitalG, Fidelity, Alphabet\",\"113\":\"SoftBank, Alibaba Group\",\"114\":\"Spark Capital, New Enterprise Associates, Goldman Sachs Investment Partners, Index Ventures, Kleiner Perkins\",\"115\":\"Greater Pacific Capital, Bessemer Venture Partners, ICONIQ Capital, Dragoneer Investment Group, Tiger Global Management\",\"116\":\"IDG\\\\u8d44\\\\u672c\\\\u3001\\\\u4e2d\\\\u4fe1\\\\u5efa\\\\u6295\\\\u8d44\\\\u672c\\\\u3001\\\\u534e\\\\u5174\\\\u521b\\\\u6295\",\"117\":\"Y Combinator, Tencent Holdings\",\"118\":\"Index Ventures, Greylock Partners\\\\u00a0, Meritech Capital Partners, Tiger Global Management, Altos Ventures\\\\u00a0, First Round Capital\",\"119\":\"Lightspeed Venture Partners, Greylock Partners, Khosla Ventures, IVP (Institutional Venture Partners), Bain Capital Ventures\",\"120\":\"\\\\u5149\\\\u4fe1\\\\u8d44\\\\u672c\\\\u3001\\\\u5947\\\\u864e360\",\"121\":\"Clayton, Dubilier & Rice\",\"122\":\"\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u534e\\\\u5e73\\\\u6295\\\\u8d44\",\"123\":\"DST Global, Naspers, Bessemer Venture Partners, Accel, Norwest Venture Partners, SAIF Partners\",\"124\":\"New Enterprise Associates, T. Rowe Price, Baillie Gifford, Revolution\",\"125\":\"Bessemer Venture Partners, Generation Investment Management, T. Rowe Price, TCV, Lead Edge Capital, Tiger Global Management\",\"126\":\"Sequoia Capital, WetSummit Capital, DFJ Growth, Silver Lake Partners, Altimeter Capital\",\"127\":\"\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\",\"128\":\"\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\\\\u3001\\\\u4e91\\\\u950b\\\\u57fa\\\\u91d1\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\",\"129\":\"Bon Angels Venture Partners, Altos Ventures, Goldman Sachs, Hillhouse Capital Group\",\"130\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\\\\u3001\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\",\"131\":\"\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u9ad8\\\\u76db\",\"132\":\"\\\\u65b0\\\\u6d6a\\\\u3001\\\\u7ea2\\\\u70b9\\\\u521b\\\\u6295\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\",\"133\":\"\\\\u524d\\\\u6d77\\\\u68a7\\\\u6850\\\\u3001\\\\u4e2d\\\\u521b\\\\u6d77\\\\u6d0b\",\"134\":\"IDG\\\\u3001\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u534e\\\\u5e73\\\\u6295\\\\u8d44\",\"135\":\"DFJ, Lux Capital, Blackbird Ventures (Australia), Thomas Tull, Grok Ventures\",\"136\":\"\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\\\\u3001H Capital\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\",\"137\":\"Google, Johnson & Johnson Development Corporation, National Institutes of Health, Sequoia Capital, GlaxoSmithKline\",\"138\":\"Daniel Klueger, Global Asset Management\",\"139\":\"\\\\u4eac\\\\u4e1c\\\\u3001\\\\u51ef\\\\u8f89\\\\u57fa\\\\u91d1\\\\u3001\\\\u8fbe\\\\u6668\\\\u521b\\\\u6295\\\\u3001\\\\u5929\\\\u56fe\\\\u8d44\\\\u672c\\\\u3001\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\",\"140\":\"Orient Hontai Capital, Kohlberg Kravis Roberts\",\"141\":\"\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\",\"142\":\"Founders Fund, Y Combinator, Generation Investment Management\",\"143\":\"Greylock Partners, Sequoia Capital, Hyundai Motor Company, Index Ventures\",\"144\":\"General Atlantic, Kohler Kravis Roberts, Tiger Global Management, Jefferies\",\"145\":\"Woodford Investment Management\",\"146\":\"Visa, General Atlantic, TA Associates, Clearstone Venture Partners , SBI\",\"147\":\"Vertex Ventures, Black Hole Capital, Funcity Capital\",\"148\":\"Goldcrest Capital, Craft Ventures, Index Ventures, Valor Equity Partners, Sequoia Capital\",\"149\":\"Accel, Index Ventures, Insight Partners, Baring Vostok Capital Partners, SNCF\",\"150\":\"Peter Thiel\",\"151\":\"New Enterprise Associates, Andreessen Horowitz, NBCUniversal, Hearst Ventures, RRE Ventures\",\"152\":\"\\\\u4e00\\\\u6c7d\\\\u96c6\\\\u56e2\\\\u3001\\\\u542f\\\\u8fea\\\\u63a7\\\\u80a1\\\\u3001\\\\u5b81\\\\u5fb7\\\\u65f6\\\\u4ee3\",\"153\":\"\\\\u56fd\\\\u6295\\\\u521b\\\\u4e1a\\\\u3001\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u8054\\\\u60f3\\\\u521b\\\\u6295\",\"154\":\"\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u534e\\\\u4eba\\\\u6587\\\\u5316\",\"155\":\"Sequoia Capital, BMW i Ventures\\\\u00a0, GV, GE Ventures, Baillie Gifford, Madrone Capital Partners\",\"156\":\"Union Square Ventures, Spark Capital, Menlo Ventures,\\\\u00a0Social Capital, Meritech Capital Partners\\\\u00a0, Tribe Capital\",\"157\":\"DST Global\\\\u00a0, Insight Partners\",\"158\":\"\\\\u5229\\\\u6b27\\\\u80a1\\\\u4efd\\\\u3001\\\\u6e90\\\\u7801\\\\u8d44\\\\u672c\\\\u3001\\\\u660e\\\\u52bf\\\\u8d44\\\\u672c\",\"159\":\"Crosslink Capital, Aspect Ventures, Cathay Innovation, Menlo Ventures, DST Global,\",\"160\":\"DH Capital, Dievini Hopp Biotech Holding, OH Beteiligungen, Bill & Melinda Gates Foundation, Baillie Gifford, Baden-W\\\\u00fcrttembergische Versorgungsanstalt f\\\\u00fcr \\\\u00c4rzte\",\"161\":\"Invoke Capital Partners, Summit Partners, Kohlberg Kravis Roberts, Talis Capital, Vitruvian Partners\",\"162\":\"IVP (Institutional Venture Partners), Venrock, Fidelity\",\"163\":\"Nexus Venture Partners, Multiples Alternate Asset Management Private Limited,Tiger Global Management, The Carlyle Group, Fosun Group, Softbank, Canada Pension Plan Investment Board\",\"164\":\"Bridgepoint, Fidelity Management and Research Company\\\\u00a0, Amazon, DST Global, General Catalyst, T. 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Ventures, Swisscanto Invest, SoftBank Investment Advisers, Kees Koolen\",\"340\":\"Viking Global Investors\",\"341\":\"Goldman Sachs Principal Strategic Investments, Khosla Ventures, August Capital, GV, ICONIQ Capital\",\"342\":\"Lewis trust Group. Rocket Internet, Kinnevik AB\",\"343\":\"Thrive Capital, IVP (Institutional Venture Partners), Index Ventures, Sequoia Capital\",\"344\":\"Softbank Investment Advisors, General Atlantic\",\"345\":\"\\\\u631a\\\\u4fe1\\\\u8d44\\\\u672c\\\\u3001\\\\u5d07\\\\u5fb7\\\\u6295\\\\u8d44\\\\u3001DCM\\\\u4e2d\\\\u56fd\",\"346\":\"Norwest Venture Partners, Kaiser Permanente Ventures, Sorenson Capital, UPMC,OrbiMed\",\"347\":\"Foxconn Technology Group, Tencent Holdings, Bharti SoftBank, Tiger Global Management\",\"348\":\"Atomic, Thrive Capital, IVP (Institutional Venture Partners)\",\"349\":\"Ginko Ventures\",\"350\":\"\\\\u5fb7\\\\u540c\\\\u8d44\\\\u672c\\\\u3001\\\\u65b9\\\\u6b63\\\\u548c\\\\u751f\\\\u3001\\\\u5bcc\\\\u5764\\\\u6295\\\\u8d44\",\"351\":\"\\\\u77e5\\\\u5408\\\\u51fa\\\\u884c\\\\u3001\\\\u9e3f\\\\u5229\\\\u667a\\\\u6c47\",\"352\":\"\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\\\\u3001Intel Capital\\\\u3001\\\\u6d77\\\\u901a\\\\u5f00\\\\u5143\",\"353\":\"\\\\u6cdb\\\\u6d77\\\\u63a7\\\\u80a1\\\\u3001\\\\u590d\\\\u661f\\\\u9510\\\\u6b63\\\\u8d44\\\\u672c\",\"354\":\"\\\\u6c49\\\\u80fd\\\\u6295\\\\u8d44\\\\u3001\\\\u8f6f\\\\u94f6\\\\u3001\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\",\"355\":\"Illumina Ventures\",\"356\":\"\\\\u5929\\\\u5e9c\\\\u96c6\\\\u56e2\\\\u3001\\\\u946b\\\\u6839\\\\u8d44\\\\u672c\",\"357\":\"Greycroft, Premji Invest\",\"358\":\"Just Eat, Movile, Warehouse Investimentos, Naspers\",\"359\":\"\\\\u5929\\\\u56fe\\\\u8d44\\\\u672c\\\\u3001\\\\u8fbe\\\\u6668\\\\u521b\\\\u6295\\\\u3001\\\\u6b63\\\\u548c\\\\u5c9b\\\\u57fa\\\\u91d1\",\"360\":\"J.P. Morgan Asset Management, Andreessen Horowitz, General Catalyst, Accel, BlackRock\",\"361\":\"Softbank Capital, Kleiner Perkins, Sherpalo Ventures\",\"362\":\"Social Capital, Bessemer Venture Partners, ICONIQ Capital, Index Ventures, Kleiner Perkins\",\"363\":\"\\\\u817e\\\\u8baf\\\\u9886\\\\u6295\\\\uff0c\\\\u4eca\\\\u65e5\\\\u8d44\\\\u672c\",\"364\":\"Saban Capital Group, Access Industries\",\"365\":\"\\\\u542f\\\\u660e\\\\u521b\\\\u6295\\\\u3001GIC\\\\u3001\\\\u9ad8\\\\u76db\",\"366\":\"Ardian, Kohlberg Kravis Roberts, Tiger Global Management\",\"367\":\"Insight Partners, Vmware\",\"368\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u4e1c\\\\u65b9\\\\u5bcc\\\\u6d77\",\"369\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u541b\\\\u8054\\\\u8d44\\\\u672c\\\\u3001\\\\u9f0e\\\\u6656\\\\u6295\\\\u8d44\",\"370\":\"\\\\u5929\\\\u56fe\\\\u8d44\\\\u672c\\\\u3001\\\\u62db\\\\u94f6\\\\u56fd\\\\u9645\\\\u3001\\\\u6d59\\\\u6c5f\\\\u91d1\\\\u63a7\",\"371\":\"\\\\u666e\\\\u6d1b\\\\u65af\\\\u3001\\\\u65b0\\\\u5e0c\\\\u671b\\\\u3001\\\\u8fdc\\\\u6d0b\\\\u8d44\\\\u672c\",\"372\":\"IDG\\\\u8d44\\\\u672c\\\\u3001\\\\u4fe1\\\\u4e2d\\\\u5229\\\\u8d44\\\\u672c\\\\u3001\\\\u952e\\\\u6865\\\\u901a\\\\u8baf\",\"373\":\"BlueRun Ventures, Mohr Davidow Ventures, Thomvest Ventures, Guggenheim Securities, SoftBank Capital, Reverence Capital Partners, Credit Suisse\",\"374\":\"Index Ventures, Scale Venture Partners, IVP (Institutional Venture Partners), Greenoaks Capital\",\"375\":\"Berkshire Partners, Norwest Venture Partners\",\"376\":\"Kohlberg Kravis Roberts, Goldman Sachs, Elephant\",\"377\":\"\\\\u963f\\\\u91cc\\\\u5df4\\\\u5df4\\\\u3001\\\\u8054\\\\u60f3\\\\u4e4b\\\\u661f\\\\u3001\\\\u597d\\\\u672a\\\\u6765\\\\u6559\\\\u80b2\\\\u96c6\\\\u56e2\",\"378\":\"IDG\\\\u3001\\\\u6b4c\\\\u6590\\\\u8d44\\\\u4ea7\",\"379\":\"\\\\u6e05\\\\u6d41\\\\u8d44\\\\u672c\\\\u3001\\\\u8944\\\\u79be\\\\u8d44\\\\u672c\\\\u3001\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\",\"380\":\"\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\\\\u3001\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\",\"381\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u4e0a\\\\u6d77\\\\u7535\\\\u6c14\\\\u3001\\\\u5174\\\\u4e1a\\\\u8bc1\\\\u5238\",\"382\":\"Naspers\",\"383\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u5149\\\\u5927\\\\u5b9e\\\\u4e1a\\\\u3001\\\\u8d5b\\\\u4f2f\\\\u4e50\",\"384\":\"Viola Ventures, Insight Partners, Goldman Sachs Private Capital Investing, ClalTech\",\"385\":\"\\\\u4e2d\\\\u6295\\\\u516c\\\\u53f8\",\"386\":\"\\\\u817e\\\\u8baf\\\\u3001\\\\u6cdb\\\\u6d77\\\\u6295\\\\u8d44\\\\u3001\\\\u4e2d\\\\u4fe1\\\\u8d44\\\\u672c\",\"387\":\"\\\\u817e\\\\u8baf\\\\u3001\\\\u5f18\\\\u6bc5\\\\u6295\\\\u8d44\",\"388\":\"IDG Capital\",\"389\":\"SoftBank Investment Advisers, SoftBank, DOMO Invest\\\\u00a0, Monashees\\\\u00a0, Dragoneer Investment Group, IFC Venture Capital Group\\\\u00a0, Iporanga Investments, Qualcomm Ventures, Microsoft\",\"390\":\"IDG\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\",\"391\":\"T. Rowe Price, Andreessen Horowitz, Deutsche Telekom, Intex Ventures, Khosla Ventures\",\"392\":\"\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u817e\\\\u8baf\",\"393\":\"DST\\\\u3001IDG\\\\u3001\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\\\\u3001DCM\",\"394\":\"Sequoia Capita, Lehman Brothers, Tenaya Capital, Wellington Management, NTT Data\",\"395\":\"Silicon Valley Bank, Goldman Sachs, Searchlight Capital Partners\",\"396\":\"Avenir Growth Capital, Eurazeo Prime Ventures\",\"397\":\"Koch Disruptive Technologies, T. Rowe Price, Hewlett Packard Enterprise, Khosla ventures, Andreessen Horowitz\",\"398\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\",\"399\":\"CITIC Securities\",\"400\":\"Hinduja Group, Leonardo DiCaprio, Venture Kick\",\"401\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u5206\\\\u4eab\\\\u6295\\\\u8d44\",\"402\":\"\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u6d77\\\\u7eb3\\\\u4e9a\\\\u6d32\",\"403\":\"\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u771f\\\\u683c\\\\u57fa\\\\u91d1\\\\u3001\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\",\"404\":\"Edison Partners,Greenspring Associates\",\"405\":\"Social Capital, Lightspeed Venture Partners, Accel, ICONIQ Capital\",\"406\":\"-\",\"407\":\"\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u534e\\\\u5174\\\\u8d44\\\\u672c\\\\u3001\\\\u5929\\\\u56fe\\\\u8d44\\\\u672c\\\\u3001\\\\u4eca\\\\u65e5\\\\u8d44\\\\u672c\",\"408\":\"\\\\u62db\\\\u94f6\\\\u56fd\\\\u9645\\\\u3001\\\\u56fd\\\\u6295\\\\u521b\\\\u65b0\",\"409\":\"T. Rowe Price, Warburg Pincus, Jackson Square Ventures\",\"410\":\"Matrix Partners India, Tata Sons Ltd, SoftBank, Tiger Global Management\",\"411\":\"Goldman Sachs Investment Partners, Kleiner Perkins, Kinnevik AB, Silver Lake Kraftwerk, Temasek Holdings, Battery Ventures, Lakestar, New Enterprise Associates, Hasso Plattner Ventures\",\"412\":\"The Carlyle Group, Benchmark, GV, Redmile Group, J.P. Morgan Asset Management, Maverick Ventures, Oak Investment Partners\",\"413\":\"Insight Partners\",\"414\":\"\\\\u8682\\\\u8681\\\\u91d1\\\\u670d\\\\u3001\\\\u8d5b\\\\u5bcc\\\\u6295\\\\u8d44\\\\u3001\\\\u677e\\\\u79be\\\\u8d44\\\\u672c\",\"415\":\"Clal Insurance Enterprises Holdings\\\\u00a0, Meitav Investment House, Intel Capital\",\"416\":\"Mayfield Fund, Trinity Ventures, DFJ Growth, Spark Capital, Lone Pine Capital\",\"417\":\"Armilar Venture Partners, North Bridge Venture Partners & Growth Equity, Goldman Sachs, Kohlberg Kravis Roberts\",\"418\":\"Mitsubishi Corp\",\"419\":\"Softbank Investment Advisors\",\"420\":\"ONE Luxury Group, Eurazeo\",\"421\":\"DST\\\\u3001\\\\u864e\\\\u6251\\\\u4f53\\\\u80b2\\\\u3001\\\\u666e\\\\u601d\\\\u8d44\\\\u672c\",\"422\":\"SoftBank Investment Advisors, Wellington Management, Premji Invest, Tiger Global Management, Inventus Capital Partners\",\"423\":\"\\\\u4e39\\\\u4e30\\\\u8d44\\\\u672c\\\\u3001\\\\u8f6f\\\\u94f6\\\\u4e2d\\\\u56fd\\\\u8d44\\\\u672c\",\"424\":\"Yuan Capital, Harbin Gloria Pharmaceuticals, Lycos Ventures\",\"425\":\"Trifecta Capital Advisors, Tiger Global Management, InnoVen Capital, Brand Capital, Kinnevik AB, Warburg Pincus, NGP Capital, Norwest Venture Partners, Omidyar Network\",\"426\":\"Accel, New Enterprise Associates\",\"427\":\"SoftBank, SoftBank Investment Advisers, Y Combinator, Andreessen Horowitz, Sequoia Capital, Delivery Hero, DST Global\",\"428\":\"\\\\u9ad8\\\\u76db\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u6ef4\\\\u6ef4\\\\u3001\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\",\"429\":\"\\\\u631a\\\\u4fe1\\\\u8d44\\\\u672c\",\"430\":\"Bain Capital Ventures, Highland Capital Partners, Kleiner Perkins, American Express Ventures, TCV, Fidelity Management and Research Company, Novel TMT Ventures, Blue Pool Capital, Temasek Holdings, Franklin Templeton Investments\",\"431\":\"500 Startups, K2 Global\",\"432\":\"SAIF Partners, Warburg Pincus\",\"433\":\"Bessemer Venture Partners, Data Collective DCVC, Future Fund\",\"434\":\"Drive Capital, Ribbit Capital, Redpoint, Tiger Global Management\",\"435\":\"QuarterMoore, Rotunda Capital Partners, Fifth Third Bancorp, Nima Capital, SUEZ Environnement, Promecap, NZ Super Fund\",\"436\":\"Jackson Square Ventures, JMI Equity, General Atlantic, Lightspeed Venture Partners\\\\u00a0, T. Rowe Price,\",\"437\":\"GIC, Tiger Global Management, Nexus Venture Partners, Helion Venture Partners\",\"438\":\"\\\\u767e\\\\u5ea6\\\\u3001\\\\u851a\\\\u6765\\\\u8d44\\\\u672c\",\"439\":\"\\\\u817e\\\\u8baf\\\\u3001\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\\\\u3001IDG\\\\u3001\\\\u7f8e\\\\u56e2\\\\u70b9\\\\u8bc4\",\"440\":\"Sutter Hill Ventures, Daimler\",\"441\":\"\\\\u542f\\\\u660e\\\\u521b\\\\u6295\",\"442\":\"\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001GIC\\\\u3001\\\\u7eaa\\\\u6e90\\\\u8d44\\\\u672c\",\"443\":\"Tao Capital Partners, Valor Equity Partners\",\"444\":\"Nvidia GPU Ventures, Tencent Holdings, Walden Venture Capital\",\"445\":\"DST Global, General Atlantic, GGV Capital, Battey Ventures\",\"446\":\"Greylock Partners, Accel, Sequoia Capital, DFJ Growth, Sapphire Ventures, Battery Ventures\",\"447\":\"Fidelity, Revolution, T. Rowe Price\",\"448\":\"Mitsubishi UFJ Financial Group, Standard Chartered Bank, BNP Paribas Private Equity\",\"449\":\"Fidelity Management and Research Company, Pitango Venture Capital, Marker, Evergreen Venture Partners\",\"450\":\"Viking Global Investors, Storm Ventures, DFJ, Salesforce Ventures\",\"451\":\"\\\\u8f6f\\\\u94f6\\\\u4e2d\\\\u56fd\\\\u3001\\\\u9ea6\\\\u987f\\\\u6295\\\\u8d44\\\\u3001\\\\u5317\\\\u6781\\\\u5149\\\\u521b\\\\u6295\",\"452\":\"Alibaba Group, DFJ\",\"453\":\"Matrix Partners, Rho Capital Partners, Shining Capital\",\"454\":\"\\\\u817e\\\\u8baf\\\\u3001\\\\u78a7\\\\u6842\\\\u56ed\\\\u521b\\\\u6295\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\",\"455\":\"General Catalyst, Institutional Venture Partners, Wellington Management, L Catterton, Glade Brook Capital Partners\",\"456\":\"Lightspeed Venture Partners, Sapphire Ventures, Khosla Ventures, General Catalysts\",\"457\":\"CapitalG, Baillie Gifford, Javelin Venture Partners, Sequoia Capital\",\"458\":\"Simone Investment Managers, NHM Invesment Corp, Kohlberg Kravis Roberts, Anchor Equity Partners\",\"459\":\"Altos Ventures, Goodwater Capital, GIC, Kleiner Perkins, Sequoia Capital China, Ribbit Capital\",\"460\":\"PayPal, Notion, Kite Ventures, Scentan Ventures, Data Collective DCVC, Wipro Ventures, Goldman Sachs Principal Strategic Investments\\\\u00a0, RTP Global, PSP Investments\",\"461\":\"GCP Capital Partners\",\"462\":\"Kleiner Perkins, SK Holdings, August Capital, IAC\",\"463\":\"Sina, Composite Capital Management\",\"464\":\"DCM\\\\u3001\\\\u8d1d\\\\u5854\\\\u65af\\\\u66fc\\\\u3001\\\\u541b\\\\u8054\\\\u8d44\\\\u672c\",\"465\":\"Lightspeed Venture Partners, DST Global\",\"466\":\"Bertelsmann, Andreessen Horowitz, CRV\",\"467\":\"\\\\u542f\\\\u660e\\\\u521b\\\\u6295\\\\u3001\\\\u9ad8\\\\u901a\",\"468\":\"\\\\u534e\\\\u5e73\\\\u6295\\\\u8d44\",\"469\":\"Softbank Investment Advisors, Blackrock, TIAA, Madrone Capital Partners, NanoDimension\",\"470\":\"Salesforce Ventures, Sutter Hill Ventures\",\"471\":\"NBC Universal, General Atlantic, Accel, Khosla Ventures\",\"472\":\"Bessemer Venture Partners, Thrive Capital, OpenView Venture Partners, Insight Partners, Brookfield Asset Management\",\"473\":\"\\\\u65b0\\\\u5929\\\\u57df\\\\u8d44\\\\u672c\\\\u3001\\\\u5149\\\\u4fe1\\\\u8d44\\\\u672c\\\\u3001IDG\\\\u3001\\\\u542f\\\\u660e\\\\u521b\\\\u6295\",\"474\":\"Gemini Israel Ventures, Scale Venture Partners, Greenspring Associates, Insight Partners, EDBI\",\"475\":\"TOM\\\\u96c6\\\\u56e2\\\\u3001Khazanah\\\\u3001IFC\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\",\"476\":\"\\\\u6d77\\\\u901a\\\\u5f00\\\\u5143\\\\u3001\\\\u5317\\\\u6781\\\\u5149\\\\u521b\\\\u6295\",\"477\":\"IDG\\\\u3001\\\\u8d5b\\\\u5bcc\\\\u57fa\\\\u91d1\\\\u3001\\\\u767e\\\\u5ea6\",\"478\":\"\\\\u7ca4\\\\u6c11\\\\u6295\\\\u3001\\\\u539a\\\\u6734\\\\u6295\\\\u8d44\\\\u3001\\\\u80e1\\\\u6da6\\\\u767e\\\\u5bcc\",\"479\":\"Partners Investment, Sky Lake Investment, Booking Holdings, GIC\",\"480\":\"\\\\u4f17\\\\u4fe1\\\\u65c5\\\\u6e38\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u521b\\\\u65b0\\\\u5de5\\\\u573a\",\"481\":\"\\\\u6d8c\\\\u94e7\\\\u6295\\\\u8d44\\\\u3001\\\\u6c47\\\\u80fd\\\\u91d1\\\\u878d\\\\u3001\\\\u78d0\\\\u77f3\\\\u8d44\\\\u672c\",\"482\":\"\\\\u51e4\\\\u51f0\\\\u3001\\\\u5c0f\\\\u7c73\\\\u3001IDG\",\"483\":\"\\\\u7f8e\\\\u56e2\\\\u70b9\\\\u8bc4\\\\u3001\\\\u817e\\\\u8baf\\\\u3001\\\\u8d1d\\\\u5854\\\\u65af\\\\u66fc\",\"484\":\"\\\\u535a\\\\u88d5\\\\u8d44\\\\u672c\\\\u3001\\\\u539a\\\\u6734\\\\u6295\\\\u8d44\\\\u3001\\\\u666e\\\\u6d1b\\\\u65af\\\\u3001\\\\u6e90\\\\u7801\\\\u8d44\\\\u672c\\\\u3001\\\\u9f0e\\\\u6656\\\\u6295\\\\u8d44\",\"485\":\"\\\\u8fdc\\\\u955c\\\\u521b\\\\u6295\\\\u3001\\\\u8d5b\\\\u5bcc\\\\u57fa\\\\u91d1\",\"486\":\"\\\\u9ad8\\\\u74f4\\\\u8d44\\\\u672c\\\\u3001\\\\u6668\\\\u5174\\\\u8d44\\\\u672c\\\\u3001\\\\u8f6f\\\\u94f6\\\\u4e2d\\\\u56fd\",\"487\":\"\\\\u541b\\\\u8054\\\\u8d44\\\\u672c\\\\u3001\\\\u6155\\\\u534e\\\\u6295\\\\u8d44\",\"488\":\"\\\\u534e\\\\u5e73\\\\u6295\\\\u8d44\\\\u3001\\\\u7ea2\\\\u6749\\\\u8d44\\\\u672c\\\\u3001\\\\u7ecf\\\\u7eac\\\\u4e2d\\\\u56fd\",\"489\":\"GPI Capital, GSO Capital Partners\",\"490\":\"\\\\u987a\\\\u4e3a\\\\u8d44\\\\u672c\\\\u3001\\\\u8fbe\\\\u6668\\\\u521b\\\\u6295\\\\u3001\\\\u534e\\\\u5e73\\\\u6295\\\\u8d44\",\"491\":\"\\\\u817e\\\\u8baf\",\"492\":\"Sequoia Capital, Visionnaire Ventures, Katalyst.Ventures\",\"493\":\"IVP (Institutional Venture Partners)\"}}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B4 课上尝试\n",
    "df = pd.read_excel(\"20春_pandas_week02_hurun_unicorn.xlsx\", encoding=\"utf8\", sheet_name=\"独角兽\")\n",
    "df.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B4 df.head()\n",
    "# B5 df.info()\n",
    "# B6 df.shape\n",
    "# B7 df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B8 df.to_markdown()\n",
    "# B9 df.to_html()\n",
    "# B10 df.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'排名': {'count': 494.0,\n",
       "  'mean': 180.97773279352228,\n",
       "  'std': 91.07319075981641,\n",
       "  'min': 1.0,\n",
       "  '25%': 84.0,\n",
       "  '50%': 224.0,\n",
       "  '75%': 264.0,\n",
       "  'max': 264.0},\n",
       " '估值（亿人民币）': {'count': 494.0,\n",
       "  'mean': 238.80566801619435,\n",
       "  'std': 623.1585372633193,\n",
       "  'min': 70.0,\n",
       "  '25%': 70.0,\n",
       "  '50%': 100.0,\n",
       "  '75%': 200.0,\n",
       "  'max': 10000.0},\n",
       " '成立年份': {'count': 494.0,\n",
       "  'mean': 2011.2348178137652,\n",
       "  'std': 3.7924771092178786,\n",
       "  'min': 2000.0,\n",
       "  '25%': 2009.0,\n",
       "  '50%': 2012.0,\n",
       "  '75%': 2014.0,\n",
       "  'max': 2019.0}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B11 df.to_dict()\n",
    "# B12 df.to_sql()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 练习B1_post-读读读的课堂后练习\n",
    "\n",
    "老手有彩蛋项目可实践\n",
    "\n",
    "IMF有[2020年1月的《世界经济展望》](https://www.imf.org/zh/Publications/WEO/Issues/2020/01/20/weo-update-january2020)，而这数据集的前一份版本都可以在其[数据入口取得](https://www.imf.org/en/data)的EXCEL数据可以下载，共用三种形式，你能用正确的参数取得数据框吗? \n",
    "* “SDMX Data” [全数据](https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/download.aspx) [zip](https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/WEOOct2019_SDMXData.zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B1 bonus\n",
    " \n",
    "# 你的代码\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![02_io_readwrite.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/02_io_readwrite.svg)\n",
    "### 读读读的小结\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![03_subset_columns_rows.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/03_subset_columns_rows.svg)\n",
    "## 如何选择表格的子集？  \n",
    "\n",
    "切切切，**切片** (英文叫slice) 是数据科学家找突破点的重要工具，是她们的数据解剖刀...\n",
    "\n",
    "参考 CheatSheet \n",
    "* Subset Observations (Rows) 列\n",
    "* Subset Variables (Columns) 行\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 切切切的代码片语\n",
    "切切切的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "\n",
    "####  列子集\n",
    "```python\n",
    "\n",
    "df.loc[]\n",
    "df.iloc[]\n",
    "df.set_index()\n",
    "df.head(n)\n",
    "df.tail(n)\n",
    "df.nlargest(n, '变量') # 最大 ， n是多少个\n",
    "df.nsmallest(n, '变量') # 最小\n",
    "df[df.估值（亿人民币）> 10]\n",
    "```\n",
    "\n",
    "#### 行子集\n",
    "```python\n",
    "\n",
    "#  行子集\n",
    "df[['变量X','变量Y','变量Z']] #可以拿来重新排序\n",
    "df[['变量X']]\n",
    "df['变量X']\n",
    "```\n",
    "\n",
    "#### 列+行子集\n",
    "```python\n",
    "\n",
    "#  行子集\n",
    "df.loc[:,['变量X':'变量Z']]    # 注意中括号里的: 和 ,的使用\n",
    "df.iloc[:,[1,2,5]]\n",
    "df.loc[df['变量X']>10, ['变量X','变量Z'] ]   \n",
    "```\n",
    "\n",
    " <b> 📒</b>\n",
    "> 行、列串列，先行再列\n",
    "\n",
    "df[\"排名\",\"企业名称\",\"估值（亿人民币）\"]].df.xxxx()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "子集代码片语说明(你来做笔记)\n",
    "* 列子集\n",
    "* 行子集\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Infor</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>JUUL Labs</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名       企业名称  估值（亿人民币）\n",
       "0   1       蚂蚁金服     10000\n",
       "1   2       字节跳动      5000\n",
       "2   3       滴滴出行      3600\n",
       "3   4      Infor      3500\n",
       "4   5  JUUL Labs      3400"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试\n",
    "df[[\"排名\",\"企业名称\",\"估值（亿人民币）\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      10000\n",
       "1       5000\n",
       "2       3600\n",
       "3       3500\n",
       "4       3400\n",
       "       ...  \n",
       "489       70\n",
       "490       70\n",
       "491       70\n",
       "492       70\n",
       "493       70\n",
       "Name: 估值（亿人民币）, Length: 494, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   排名        2 non-null      int64 \n",
      " 1   企业名称      2 non-null      object\n",
      " 2   估值（亿人民币）  2 non-null      int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 64.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.loc[[0,2],[\"排名\",\"企业名称\",\"估值（亿人民币）\"]].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   排名  企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "0   1  蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1   2  字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2   3  滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "\n",
       "                   部分投资机构  \n",
       "0          春华资本、中投海外、红杉资本  \n",
       "1     红杉资本、海纳亚洲、纪源资本、启明创投  \n",
       "2  腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"估值（亿人民币）\"]> 3500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![03_subset_columns_rows.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/03_subset_columns_rows.svg)\n",
    "### 切切切的小结\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "## 如何在熊猫中绘图？\n",
    "\n",
    "> <mark>绘绘绘</mark>，**绘图** ( 数据框.plot() ) 是数据科学家**以数据框为中心**的代码实践，减少以图表类型为开头的编程思维来作图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘绘绘的代码片语\n",
    "绘绘绘的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "df.plot()\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a5474e0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20272 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20540 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20159 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20154 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 27665 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 24065 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20272 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20540 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20159 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20154 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 27665 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 24065 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/Users/autumnhui/anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    }
   ],
   "source": [
    "df[[\"估值（亿人民币）\"]].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b2885c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl  \n",
    "mpl.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签  \n",
    "mpl.rcParams['axes.unicode_minus']=False #用来正常显示负号 \n",
    "\n",
    "df.loc[[0,1,2,3,4,5],[\"估值（亿人民币）\"]].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04_plot_overview.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/04_plot_overview.svg)\n",
    "### 绘绘绘的小结\n",
    "\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![05_newcolumn_2.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/05_newcolumn_2.svg)\n",
    "## 如何从现有列创建派生新列？\n",
    "> <mark>列列列</mark>，派生新列意谓着变数variables的进一部转换，是数据科学家按步就班做ETL的过程，新派生列就是**变数variables**的转换\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列列列的代码片语\n",
    "列列列的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "df['新变量'] = df['变量X'] + df['变量Y']\n",
    "df['新变量'] = [ 转换(x) for x in df['变量Y'] ]     # 列表推导转换\n",
    "\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![05_newcolumn_2.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/05_newcolumn_2.svg)\n",
    "### 列列列的小结\n",
    "\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "\n",
    "## 如何计算汇总描述性统计信息？\n",
    "算算算，描述性统计竟然代码可以这麽容易....，但难的仍是在数据科学家的数据定义及解释上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 算算算的代码片语\n",
    "算算算的代码片语 (code snippets)，新手请认真记忆，**注意标点及缩进**\n",
    "\n",
    "```python\n",
    "\n",
    "df.describe()\n",
    "df.describe(include=all)\n",
    "\n",
    "df.count()\n",
    "df.sum()\n",
    "\n",
    "df.min()\n",
    "df.max()\n",
    "df.mean()\n",
    "df.median()\n",
    "\n",
    "df.var()\n",
    "df.std()\n",
    "```\n",
    "\n",
    "代码片语说明\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![06_groupby.svg](https://pandas.pydata.org/pandas-docs/version/1.0.2/_images/06_groupby.svg)\n",
    "### 算算算的小结预告\n",
    "\n",
    "1. 1\n",
    "2. 2\n",
    "3. 3\n",
    "\n",
    "<div class=\"emoticon\">😃😄😁</div>\n",
    "\n",
    "----- \n",
    "----- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 本周我的总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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  "toc": {
   "base_numbering": 1,
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   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
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    "left": "223px",
    "top": "164px",
    "width": "165px"
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